Data management is a method that involves the creation and enforcement of procedures, policies and processes to manage data throughout its entire life cycle. It ensures that data is useful and accessible, assists in compliance with regulations and enables informed decisions.
The importance of effective data management has grown significantly as organizations automate their business processes, leverage software-as-a-service (SaaS) applications and deploy data warehouses, among other initiatives. The result is a proliferation of data that needs to be consolidated and pushed to business intelligence (BI) and analytics systems as well as enterprise resource planning (ERP) platforms, Internet of Things (IoT) sensors, machine learning and generative artificial intelligence (AI) tools to provide advanced insights.
Without a well-defined and standardized data management plan, businesses can end up with incompatible data silos and inconsistency of data sets that hamper the ability to run business intelligence and analytics applications. Inadequate data management can affect the trust of customers and employees.
To address these issues, companies must develop a data-management plan (DMP) that includes the people and processes needed to manage all types look what i found data. A DMP can, for instance can assist researchers in determining the appropriate file name conventions they should utilize to arrange data sets to preserve them over time and make them simple to access. It could also include data workflows that specify the steps to follow for cleansing, validating and integrating raw data sets and refined data sets in order to make them suitable for analysis.
A DMP can be utilized by companies that collect consumer data to ensure compliance with privacy laws on a global and state level, like the General Data Protection Regulation of the European Union or California’s Consumer Privacy Act. It can be used to guide the development and implementation of policies and procedures to address security threats to data.